Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Methods: Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups. Results: Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°. Conclusions TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

BACKGROUND: Delayed platelet engraftment is a common complication after umbilical cord blood transplantation (UCBT), and there is no standard therapy. Avatrombopag (AVA) is a second-generation thrombopoietin (TPO) receptor agonist (TPO-RA) that has shown efficacy in immune thrombocytopenia (ITP). However, few reports have focused on its efficacy in patients diagnosed with thrombocytopenia after allogeneic hematopoietic stem cell transplantation (allo-HSCT). METHODS: We conducted a retrospective study at the First Affiliated Hospital of the University of Science and Technology of China to evaluate the efficacy of AVA as a first-line TPO-RA in 65 patients after UCBT; these patients were compared with 118 historical controls. Response rates, platelet counts, megakaryocyte counts in bone marrow, bleeding events, adverse events and survival rates were evaluated in this study. Platelet reconstitution differences were compared between different medication groups. Multivariable analysis was used to explore the independent beneficial factors for platelet implantation. RESULTS: Fifty-two patients were given AVA within 30 days post-UCBT, and the treatment was continued for more than 7 days to promote platelet engraftment (AVA group); the other 13 patients were given AVA for secondary failure of platelet recovery (SFPR group). The median time to platelet engraftment was shorter in the AVA group than in the historical control group (32.5 days vs . 38.0 days, Z  = 2.095, P  = 0.036). Among the 52 patients in the AVA group, 46 achieved an overall response (OR) (88.5%), and the cumulative incidence of OR was 91.9%. Patients treated with AVA only had a greater 60-day cumulative incidence of platelet engraftment than patients treated with recombinant human thrombopoietin (rhTPO) only or rhTPO combined with AVA (95.2% vs . 84.5% vs . 80.6%, P  <0.001). Patients suffering from SFPR had a slightly better cumulative incidence of OR (100%, P  = 0.104). Patients who initiated AVA treatment within 14 days post-UCBT had a better 60-day cumulative incidence of platelet engraftment than did those who received AVA after 14 days post-UCBT (96.6% vs . 73.9%, P  = 0.003). CONCLUSION Compared with those in the historical control group, our results indicate that AVA could effectively promote platelet engraftment and recovery after UCBT, especially when used in the early period (≤14 days post-UCBT).
Humans ; Female ; Male ; Thrombocytopenia/etiology* ; Adult ; Retrospective Studies ; Cord Blood Stem Cell Transplantation/adverse effects* ; Middle Aged ; Adolescent ; Young Adult ; Thiazoles/adverse effects* ; Platelet Count ; Receptors, Thrombopoietin/agonists* ; Child ; Thiophenes

Evidence mapping was used to systematically analyze the clinical research evidence of oral Chinese patent medicines in the treatment of intracerebral hemorrhage(ICH), thus revealing the distribution and quality of evidence in this field. The relevant articles were retrieved from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase, Cochrane Library, and Web of Science from inception to July 5, 2024. The distribution characteristics of evidence were presented numerically and graphically. A total of 35 Chinese patent medicines were identified, involving 261 articles. The basic information of the 35 Chinese patent medicines, publication trend, traditional Chinese medicine(TCM) syndromes, interventions, and outcome indicators were compared and analyzed, and the methodological quality of the articles was evaluated. The results indicated that the clinical scope of Chinese patent medicines in the treatment of ICH was broad. However, the available studies inadequately emphasized the advantages and characteristics of TCM, lacked the safety information and the standards for evaluating outcome indicators, and paid insufficient attention to cognitive ability and neuropsychology. In addition, these articles demonstrated low quality. It is recommended that follow-up clinical research should be standardized and highlight the characteristics of TCM. In the analysis of outcome indicators, TCM syndrome evaluation should be taken as an important outcome indicator, and the evaluation criteria should be unified. Moreover, more attention should be paid to patients' cognitive ability and neuropsychology. The holder of marketing license of Chinese patent medicines should standardize the clinical position and improve the safety information in the medicine instructions according to the relevant requirements of the National Medical Products Administration. Additionally, the proportion of Chinese patent medicines in the category A list of medical insurance should be increased, and the limited medical resources should be rationally allocated.
Cerebral Hemorrhage/drug therapy* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional ; Nonprescription Drugs/administration & dosage* ; Administration, Oral